Application of the NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease using Cerebrospinal Fluid Biomarkers in the AIBL Study

IF 8.5 3区 医学 Q1 CLINICAL NEUROLOGY Jpad-Journal of Prevention of Alzheimers Disease Pub Date : 2019-01-01 DOI:10.14283/jpad.2019.25
S. Burnham, Preciosa M. Coloma, Qiao-Xin Li, Steven J. Collins, G. Savage, Simon M. Laws, Simon M. Laws, J. Doecke, P. Maruff, R. Martins, R. Martins, D. Ames, Christopher Rowe, Colin L. Masters, V. Villemagne
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引用次数: 26

Abstract

BackgroundThe National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease.ObjectivesTo stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)).DesignIndividuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A−T−(N)−; A+T-(N)−; A+T+(N)−; A+T−(N)+; A+T+(N)+; A−T+(N)−; A−T−(N)+; A−T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers.SettingTwo study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study.ParticipantsOne-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays.Intervention (if any)Not applicable.MeasurementsThree CSF biomarkers, namely amyloid β1–42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test–Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores.ResultsThirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures.ConclusionsIncreasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials.
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NIA-AA研究框架的应用:在AIBL研究中使用脑脊液生物标志物对阿尔茨海默病的生物学定义
美国国家衰老和阿尔茨海默病协会(NIA-AA)提出了一个新的研究框架:迈向阿尔茨海默病的生物学定义,该框架使用三种生物标志物构建:a ß-淀粉样蛋白,tau和神经变性AT(N),以产生基于生物标志物的阿尔茨海默病定义。目的采用新的NIA-AA研究框架,利用脑脊液(CSF)生物标志物对AIBL患者进行分层。目的:评价AT(N)分层引起的不同组的临床和认知特征。将结果与使用双生物标志物构建标准(AT和/或A(N))分层的结果进行比较。设计将个体分为A、T、(N)类阳性或阴性,然后分配到适当的AT(N)组合组:A−T−(N)−;A + T - (N)−;A + T + (N)−;A + T−(N) +;+ T + (N) +;−−T + (N);−−T (N) +;−T + (N) +。根据NIA-AA研究框架,这8个AT(N)组随后被分解为4个主要的感兴趣组(正常AD生物标志物、AD病理改变、AD和非AD病理改变),并评估每组4.5年的临床和认知轨迹。在两个敏感性分析中,根据AT生物标志物和A(N)生物标志物的阳性或阴性将个体分为四组后,重复了这些方法。背景:澳大利亚墨尔本(维多利亚)和珀斯(西澳大利亚)的两个研究中心从初级保健医生或三级记忆障碍诊所招募MCI患者和AD患者。认知健康的老年nc是通过广告或通过研究参与者的配偶招募的。参与者:来自AIBL研究的140名NC、33名MCI和27名AD患者使用Elecsys®检测方法进行了CSF评估。干预(如有)不适用。测量三种脑脊液生物标志物,即淀粉样蛋白β1-42、磷酸化tau181和总tau,以提供AT(N)分类。临床和认知轨迹评估使用AIBL临床前阿尔茨海默氏认知复合(AIBL- pacc),言语情景记忆复合,执行功能复合,加州言语学习测试第二版;长延迟自由回忆,迷你精神状态检查,和临床痴呆评定盒子得分总和。结果:38%的老年nc没有AD生物标志物异常的证据,33%的生物标志物水平与AD或AD病理改变一致,29%的生物标志物有非AD生物标志物变化的证据。在NC参与者中,那些有AD病理生物标志物证据的人在认知结果评估中的表现往往比其他生物标志物组更差。大约四分之三的MCI或AD参与者的生物标志物水平与研究框架对AD或AD病理改变的定义一致。对于MCI参与者,随着异常生物标志物的增加,AIBL-PACC评分下降;在一些认知测量中,异常生物标志物的增加也与下降率的增加有关。结论生物标志物异常的增加与认知轨迹的恶化有关。生物标志物分类的实施有助于在临床实践中更好地描述预后,并识别那些更有可能在临床进展的高危个体,以便将其纳入未来的治疗试验。
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期刊介绍: The JPAD « Journal of Prevention of Alzheimer’Disease » will publish reviews, original research articles and short reports to improve our knowledge in the field of Alzheimer prevention including : neurosciences, biomarkers, imaging, epidemiology, public health, physical cognitive exercise, nutrition, risk and protective factors, drug development, trials design, and heath economic outcomes. JPAD will publish also the meeting abstracts from Clinical Trial on Alzheimer Disease (CTAD) and will be distributed both in paper and online version worldwide.
期刊最新文献
Burden of Illness in People with Alzheimer's Disease: A Systematic Review of Epidemiology, Comorbidities and Mortality. Are Population-Level Approaches to Dementia Risk Reduction Under-Researched? A Rapid Review of the Dementia Prevention Literature. Expectancy Does Not Predict 18-month Treatment Outcomes with Cognitive Training in Mild Cognitive Impairment. Lifestyle and Socioeconomic Transition and Health Consequences of Alzheimer's Disease and Other Dementias in Global, from 1990 to 2019. Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets.
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